# TAPE: Learning General Audio Effect Representations

**Authors:** Phillip Long, Yongyi Zang, Qihui Yang, Christodoulos Benetatos, Junfei Liu, Randal Leistikow, Julian McAuley, Perry R. Cook  
**Lab:** University of California San Diego · **Published:** 29 Oct 2026  
**Links:** [arXiv](https://arxiv.org/abs/2610.40759) · [PDF](https://arxiv.org/pdf/2610.40759) · [This page](https://omen-bit.github.io/ai-paper-daily/paper/2610.40759.html)

## Key numbers

- **275×**: while running 275× faster than that search
- **1.8×**: a C++ implementation with the trained weights processed 128-sample buffers 1.8× faster than real time

## Abstract

Musicians and mixing engineers often can decipher the signal processing chain they hear on a reference recording, then adapt it to new material. We present TAPE (Transformations of Audio Parameter Embeddings), which estimates this processing from the processed reference audio alone. An encoder trained to identify processors in eight major effect families maps the reference to an embedding. A differentiable digital signal processing (DSP) chain decodes the embedding into editable parameters in one forward pass and applies them to a different unprocessed recording. Its modules are equalization, compression, waveshaping, a small residual network, and reverberation. TAPE achieves lower spectral error than DeepAFx-ST, the previous state of the art for single-step regression, and lower spectral error than per-example search over a chain approximating ST-ITO's, while running 275× faster than that search. Because the decoder outputs DSP parameters, its chain can also run causally: a C++ implementation with the trained weights processed 128-sample buffers 1.8× faster than real time, with no missed deadlines.

---

Notes written by AI from the paper, numbers copied from the abstract. Check the paper before you rely on them. From AI Papers Daily.